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\section{Introduction}
\subsection{Goals of the course}
\begin{itemize}
    \item Understand BCI
    \item Overview the content of the whole course
    \item Arouse interest in BCI
\end{itemize}

\subsection{Principle of Brain Computer Interface}
\paragraph{What is Brain Computer Interface}
Brain Computer Interface (BCI) is a system that can allow users to communicate with the surrounding
directly by their brain. It is a direct communication pathway between the
brain's electrical activity and an external device, most commonly a computer or robotic limb.
\paragraph {} \par
They work in three main steps: collecting brain signals, interpreting them and
outputting commands to a connected machine according to the brain signal received. BCI can be
applied to a variety of tasks, including but not limited to neurofeedback, restoring motor
function to paralyzed patients, allowing communication with locked in patients and improving
sensory processing. BCI can be separated in three categories depending on the method used to
collect brain signals.
\paragraph{Three types of BCI}

\begin{figure}[htbp]
    \centering
    \subfigure[Non-Invasive BCI]{
        \begin{minipage}[t]{0.325\linewidth}
            \centering
            \includegraphics[width=3cm]{img/Non-Invasive BCI.jpeg}
            % \caption{}
        \end{minipage}%
    }%
    \subfigure[Semi-invasive BCI]{
        \begin{minipage}[t]{0.325\linewidth}
            \centering
            \includegraphics[width=2.5cm]{img/Semi-invasive BCI.png}
            % \caption{}
        \end{minipage}%
    }%
    \subfigure[Invasive BCI]{
        \begin{minipage}[t]{0.325\linewidth}
            \centering
            \includegraphics[width=3cm]{img/Invasive BCI.jpeg}
            % \caption{}
        \end{minipage}
    }%
    \caption{Types of BCI}
\end{figure}
\begin{itemize}
    \item {\large Invasive BCI }
          \paragraph{}
          The sensors are placed on the scalp to
          measure the electrical potentials produced
          by the brain (EEG) or the magnetic field (MEG).
    \item {\large Semi-Invasive BCI}
          \paragraph{}
          The electrodes are placed on the exposed surface of the brain(ECoG).
    \item { \large Non-Invasive BCI}
          \paragraph{}
          The micro-electrodes are placed directly into the cortex, measuring
          the activity of a single neuron.
\end{itemize}

\paragraph{What are the components in the framework of a BCI system?}
\paragraph{}
There are four components in a BCI system.The system is mainly responsible for four functions:
Recording, Identifying, Transforming and Responding.
\begin{enumerate}
    \item {\large Recording}
          \paragraph{}
          Recording system will recording and acquire analog signals from your brain using
          different sensors. And the system will feed the recorded brain signals into a computer.
    \item {\large Identifying}
          \paragraph{}
          The received signal is then amplified and filtered in order to remove the noise from the signal and enhance the components that
          are associated with the subject's intention(we call this preprocess).
          And we will do feature extraction which is the process of extracting the unique features from the received signal. These
          features should have strong correlations with the user’s intent. Finally we do Classification dividing the brain signals into
          different groups.
          \paragraph{}
          This step is considered to be the most critical step in a BCI system.

    \item {\large Transforming}
          \paragraph{}
          This step uses steps such as pattern recognition or machine learning to convert pattern data into control signals
          that will control some external device and make the device respond accordingly。

    \item {\large Responding}
          \paragraph{}
          The control feedback from the device is accepted, and the brain responds further to the feedback.
\end{enumerate}
\paragraph{BCI is not a mind-reading system but a pattern-reading system.}
\paragraph{}
BCI system didn't process the particular intentions directly ，but recognize particular brain
signal patterns.By identifying the special pattern in the EEG signal, BCI can obtain the
corresponding signal pattern, so that the machine can make corresponding feedback for the special signal.

\section{Basic Neurosicience}
\subsection{Brain Function and Neurons}
\paragraph{Brain}
The brain weighs about 3 pounds.But it comsumes about 20 percent of the body's energy.
And the main funtion of our brain is to enact a behavior.By Transforming signals from millions of sensorsl
located all over the body into appropriate muscle commands to enact a behavior suitable to the task at hand.
\paragraph{Brian Function}
And the neuron is the main "worker" of brain. It could process infomations in parallel.
It is a complex electrochemical device which receives infomation from other neurons, process this infomation and conveys its
output to hundreds of other neurons.Meanwhile, the connections between neurons are plastic which allows the
neuron network adapting to the different input and new circumstances.
\paragraph{Neurons}
A neuron is a type of cell that is generally regarded as the basic computitional unit of the nervous system.
The funtion of a neuron is to process and transmit informations. Neurons receive excitatory or inhibitory input from
other cell or from physical stimuli. When the neuron receives sufficiently strong inputs from others neurons, a cascade of events
if trigered, resulting in rapid rise and fall of the memberane potential. The memberane potential is called action potential or spike.
which is a critical way of communication between one neuron and another.
\begin{figure}[htbp]
    \begin{center}
        \includegraphics[width=4cm]{img/membrane potential.jpeg}
    \end{center}
    \caption{Membrane Potential}
\end{figure}

\subsection{Neurons Basic Structure and Function}
\paragraph{Action Potential}
In the absence input, most of the neurons have a resting potential, which is around -60mV.
But inhibitory or excitatory inputs will cause changes to the resting potentials, which we called graded potential
that may cause excitatory potential ot inhibitory potential.
\paragraph{}
And when the summary of the volume up over the threshold volume, the action potential will be start at the
trigger zone and conducted all the way down the axon. The funny fact is that regardless of the distance of that
axon, the intensity of the action potential remain the same.
\begin{figure}[htbp]
    \begin{center}
        \includegraphics[width=6cm]{img/action potiental.png}
    \end{center}
    \caption{Action Potential}
\end{figure}
\pagebreak
\paragraph{Dendrites and Axons}
A neuve cell is compused of soma, dendrites and axon.
In the term of length, dendrites tend to be smaller than axons.
And at the end of axon, there is a structure called axon terminals which
could connect to other nerve cells or other kind of cells to transmit signals.
The connections between nerve cells made up our existing nervous system.
\paragraph{Synapses}
Neurons communicate with each other through connections known as synapse, which could be electrical
but typically chemical. A synapse is a gap between a axon of one neuron and a dendrites of another cell.
Neurons are specialized to pass signals to individual target cells, and synapses are the means by which they do so.
\begin{figure}[htbp]
    \begin{center}
        \includegraphics[width=10cm]{img/synapes.png}
    \end{center}
    \caption{Action Potential}
\end{figure}
\subsection{Adapting the Connections: Synapses Plastical}
\paragraph{Synapses Plastical}
A critical component of the brain’s adaptive capabilities is the ability of neurons to change
the strength of the connections between neurons is \textbf{synapses plasticity}.
We can divide synapses plasticity into two categories based on duration.
\begin{itemize}
    \item Long-term plasticity
          \paragraph{}
          This kind of plasticity have been experimentally observed.
          The most studied being Long-term depression (LTD) and Long-termpotentiation (LTP).
          Both two changes to synapses that last for hours or even days.
    \item Short-term plasticity
          \paragraph{}
          Short-term synaptic plasticity acts on a timescale of tens of milliseconds to a few minutes
          unlike long-term plasticity, which lasts from minutes to hours. Short term plasticity can either strengthen or weaken a synapse.
          This type of plasticity could be divided into two types: spike timing dependent plasticity(STDP) and short-term facilitation/depression (STF/STD).
\end{itemize}

\subsection{Brain Organization, Anatonmy and Function}
\paragraph{}
The design of BCI is based on the organization of barin.
BCI is aimed at capturing the signals and stimulate our brain so as to take control
of machines.
Each brain hemisphere (parts of the cerebrum) has four sections, called lobes:
frontal, parietal, temporal and occipital. Each lobe controls specific functions.
And there are many deeper structure in out brain, but we don't discuss it in this lecture.

\begin{figure}[htbp]
    \begin{center}
        \includegraphics[width=8cm]{img/barin anatomy.png}
    \end{center}
    \caption{Basic Structure of Brain}
\end{figure}

\section{Recording and Stimulating the Brain}
\subsection{Recording Signals from the Brain}
\paragraph{Invasion Techniques}
The invasion recording method,which is already widely uesd, would be used to record
signals from individual nervous of the brain. When a part of
skull is removed, an electrode can be placed in the brain,
then we cover the brain with the skull again.
\paragraph{}
Also, we carried on some exprimences on animals, but it differed from the same exprimences
on human. In general, we hope that experiments on humans will do less harm.
Also, because there is no pain receptor near the human brain, it does not cause pain in experiments.
\paragraph{}
Compared to non-invasion technologies, invasion technologies allow recording of action potentials at
milliseconds timescale, and it also measures indirect correlates of neural activity.
In clinical trials, we typically use tiny electronic chips implanted into brain tissue.
Microelectrodes generally consist of small electrical wires.
And there are two types of recording techniques to capture the signals.
\paragraph{Intracellular Recording}
Intracellular recording measures the potential difference bewtwwen
the inside of the cell and an external electrode placed in the solution bathing
the neuron. In other words, it measures the ionic currents of the cell.
\paragraph{Extracellular Recording}
This kind of technique measures the potential difference between the tip of the
extracullular eletrode, which was placed near but outside a neuron), and a ground eletrode.
\begin{figure}[htbp]
    \begin{center}
        \includegraphics[width=10cm]{img/tracellular_recordings.png}
    \end{center}
    \caption{Tracellular Recordings}
\end{figure}
\pagebreak
\subsection{Multielectrode Array}
\paragraph{Advantages \& Disadvantages}
Multielectrode array has more increased spatial resolution, and it
extracts more complex types of information, which could be useful for controlling
prosthetic devices. But it cause scars to the brain tissue and increases impedance of the
electrodes. Typically, we try other sybstances to improve the condoctivity of electricity of
our multielectrode array.
\paragraph{ECoG}
In this way ,we could generate Electrocoticograph which recording brain signals
involving placing electrode on the surface of the brain.
Sometimes, this kind of techniques is classified as Semi-invasive techniques.
\begin{figure}[htbp]
    \begin{center}
        \includegraphics[width=16cm]{img/invasion_chips.jpeg}
    \end{center}
    \caption{Invasion BCI Microelectrodes}
\end{figure}


\subsection{EEG $\& $ MEG $\& $ fMRI $\& $ fNIR }
\paragraph{EEG}
Electroencephalography (EEG) is a popular kind of non-invasion technique for recording signals
from the brain using electrodes by wrapping the whole scalp. It has relatively
higher temporal resultion, but poor spatial resolution.


\paragraph{MEG}
Magnetoencephalogtaphy (MEG) measurses the magnetic fields produced By
electrical activity in the brain using SQUIDs.It originate from the
net effect tof ionic currents flowing in the dendries of neurons.
And complementary to EEG, MEG offers high temporal resolution.
But it is very demanding for the laboratory, it is not only bulky
and requires the construction of a magnetic field laboratory.


\paragraph{fMRI}
Functional Magnetic Resonance Imaging(fMRI) indirectly measures neural activity in the brain
by detecting changes in blood flow due to increased activation of neurons in particular brain areas
during specific tasks.It generate images showing activation in the brain.
By given the oxygenation levels in the blood, the signal recorded by fMRI is
called the blood exygenation level dependent response. It has very high spatial resolution
but relatively lower temporal resolution.

\paragraph{fNIR}
fNIR is short for Functional Near Infrared Imaging. This kind of technique records the signals using infrared light which can penetrate the skull
and enter a few centimeters into the cortex. It shares many similarities with EEG. It measure neural
activity only close to the skull.

\subsection{Simultaneous Recording and Stimulation}
\paragraph{}
Nowadays, researchers are no longer limited to studying
how to record brain waves and recording the feedback.
We're looking for ways to use our brainwaves in other ways.
Now there are people using Multielectrode arrays to take information
and convert it into electrical information, and there are people using
neurochip to take information from the cerebral cortex.
Experiments have been going on, and they've made a lot of progress.
\paragraph{}
\paragraph{}
\paragraph{}
\begin{figure}[htbp]
    \centering
    \begin{minipage}{0.49\linewidth}
        \centering
        \setlength{\abovecaptionskip}{0.28cm}%表示caption与图片之间的距离
        \includegraphics[width=5cm]{img/EEG.png}
        \caption{EEG}
        \label{chutian1}%文中引用该图片代号
    \end{minipage}
    \begin{minipage}{0.49\linewidth}
        \centering
        \includegraphics[width=5cm]{img/FMRI.png}
        \caption{FMRI}
        \label{chutian2}%文中引用该图片代号
    \end{minipage}
    \begin{minipage}{0.49\linewidth}
        \centering
        \includegraphics[width=5cm]{img/FNIR.png}
        \caption{FNIR}
        \label{chutian3}%文中引用该图片代号
    \end{minipage}
    \begin{minipage}{0.49\linewidth}
        \centering
        \includegraphics[width=5cm]{img/MEG.png}
        \caption{MEG}
        \label{chutian4}%文中引用该图片代号
    \end{minipage}
\end{figure}

\pagebreak
\section{Signal Processing}
\subsection{Spike Sorting}
We isolate and extract the spikes that being emitted by a signal
neuron recording electrode. And we classify spikes according to
their peak amplitude. Nowadays, we use Window Discriminator Method and
Automatically Clustering Method to classify and process the chaotic
electrical signals in the brain.
\begin{figure}[htbp]
    \begin{center}
        \includegraphics[width=10cm]{img/Basic_step_processing_signals.png}
    \end{center}
    \caption{Spike Sorting}
\end{figure}

\subsection{Several Methods for Signal Processing}
\paragraph{FFT}
The Fast Fourier transform(FFT) means that you can express a function
that satisfies certain conditions as a trigonometric
function (sine and/or cosine) or a linear combination of their integrals.
Using this algorithm can greatly reduce the number of multiplications
required by the computer to compute the discrete Fourier transform.
In particular, the more sampling points N is transformed,
the more significant the computational cost saving of FFT algorithm is。
\paragraph{Wavelet Transform}
Wavelet transform (WT) is a novel wavelet transform analysis method.
It inherits and develops the idea of short-time Fourier Transform localization.
At the same time, it overcomes the shortcomings such as the window size
does not change with frequency, etc. It can provide a "time-frequency" window
that changes with frequency, which is an ideal tool for signal time-frequency analysis and processing.
\paragraph{Time Domain Analysis}
Time domain analysis is to analyze the stability, transient and steady-state
performance of the control system according to the time domain expression of
the output under a certain input. As the time domain analysis is a direct method
to analyze the system in the time domain, so the time domain analysis has the
advantages of intuitive and accurate. The time domain representation of system
output can be obtained by differential equation or transfer function.
\paragraph{Bayesian Filtering}
Bayesian Analysis is the basis of Bayesian learning, which provides a
method for calculating the probability of a hypothesis based on the
prior probability of a hypothesis, the probability of observing different
data for a given hypothesis, and the observed data itself.
\paragraph{PCA}
PCA (principal components analysis), also known as principal
component analysis, aims to convert multiple indicators into
a few comprehensive indicators by using the idea of dimension reduction.
In statistics, principal component analysis
(PCA) is a technique for simplifying data sets.
It's a kind of linear transformation.
\subsection{Artifact Reduction Techniques}
\paragraph{Defination}
The use of metal artifact reduction sequence software allows
the identification of adverse soft tissue reaction in the form of a mass,
fluid collection, or muscle destruction.\cite{ref2}
\paragraph{Linear Model}
Assuming that the effect artifacts is additive, we could remove EOG by
following formula. And using PCA, ICA can achieve the effect.
\begin{equation}
    \begin{split}
        EEG_i(t) = EEG_i^{true}(t) + K \cdot EOG(t)
        \\
        EEG_i^{true}(t) = EEG_i(t) - K \cdot EOG(t)
    \end{split}
\end{equation}
\begin{figure}[htbp]
    \begin{center}
        \includegraphics[width=7cm]{img/artifact_reduction.png}
    \end{center}
    \caption{Artifact Reduction using Linear Model \cite{ref1}}
\end{figure}

\pagebreak
\section{Machine Learning}
\paragraph{}{Role of Machine Learning for BCI}
Machine learning focuses on how computers can
imitate or implement human learning behavior
in order to acquire new knowledge or skills,
and reorganize existing knowledge structures to
continuously improve their performance.
After data preprocessing, we transform signals
into control commands by means of machine learning.
The common approaches are classification and regression.
\subsection{Classification Techniques}
\paragraph{Binary Classification}
The goal of binary classification is to implement
a classifier that, given an input, can output a
label of 0 or 1. The basic classification methods
are LDA, RDA, QDA, neural network method and support
vector machine.
\begin{figure}[htbp]
    \begin{center}
        \includegraphics[width=7cm]{img/svm.png}
    \end{center}
    \caption{Support Vector Machine\cite{ref4}}
\end{figure}

\paragraph{Ensemble Classification}
Ensemble learning is not a specific algorithm,
but rather one or more strategies adopted in
machine learning to improve prediction accuracy.
The principle is to build multiple weakly supervised
models and use certain strategies to obtain a better
and more comprehensive strong supervised model.
Several individual learners are obtained by training,
and an ensemble learner is obtained by a certain strategy.
Commonly used algorithms are Bagging, Boosting ,
Random Forests, Stacking etc.
\paragraph{Multi-Class Classification}
Multi-Class classification is the problem of
classifying instances into one of three or more classes.
Unlike binary classification, multiple classifiers are
used to classify the input data into multiple classes
instead of two, but we can also use the one-to-many
idea and treat all the remaining classes as the other
classes of the current class. Commonly used methods are
KNN, Naive Bayes, LVQ, etc.
\subsection{Regrassion Techniques}
Regression is used to predict the relationship between
input variables (independent variables) and output
variables (dependent variables), specifically when
the value of the input variable changes and the consequent
change in the value of the output variable.
A regression model is simply a function that
represents a mapping from an input variable to an output variable.
\paragraph{Linear Regression}
Linear regression is a statistical analysis method that uses
regression analysis in mathematical statistics to determine the
quantitative relationship between two or more variables, which
is widely used. In regression analysis, only one independent
variable and one dependent variable are included, and the
relationship between them can be approximately represented by a
line.
\paragraph{}This regression analysis is called unary linear regression
analysis. If two or more independent variables are included in
the regression analysis and there is a linear relationship between
the dependent variable and the independent variables, it is called
multiple linear regression analysis.
\paragraph{}
Just as there are linear models, there are nonlinear models,
which are used to fit a nonlinear function. It also plays a great role
in brain signal fitting, and nonlinear fitting also plays a role in
other fields.

\begin{figure}[htbp]
    \centering
    \subfigure[Linear Model]{
        \begin{minipage}[t]{0.45\linewidth}
            \centering
            \includegraphics[width=6cm]{img/linear-regression.png}
        \end{minipage}
    }
    \subfigure[Non-Linear Model]{
        \begin{minipage}[t]{0.45\linewidth}
            \centering
            \includegraphics[width=6cm]{img/non-linear regression.png}
        \end{minipage}
    }
    \caption{Linear Regression}
\end{figure}
\paragraph{Neural Networks and BP}
Artificial Neural Networks (ANNs),
also referred to as neural networks (NNs)
is a mathematical model that mimics the behavioral
characteristics of animal neural networks and performs
distributed parallel information processing. This network
depends on the complexity of the system, by adjusting the
interconnection between a large number of internal nodes,
so as to achieve the purpose of processing information.
\paragraph {Backpropagation (BP)} is a method of optimizing neural networks
based on gradient descent. Its design reduces the exponential
explosion of repeated subexpressions and also reduces the memory
overhead, so it is a high performance optimization algorithm.
\begin{figure}[htbp]
    \centering
    \includegraphics[width=13cm]{img/NN BP.png}
    \caption{Neural Networks and BP}
\end{figure}
\paragraph{Gaussian Processes}
Gaussian process is a kind of random process in probability
theory and mathematical statistics, which is the combination
of a series of random variables obeying normal distribution in an exponential set.
If the uncertainty is prediction is high, the BCI can choose
not to execute the command , preventing a potentially catastrophic accident.

\begin{figure}[htbp]
    \begin{center}
        \includegraphics[width=6cm]{img/GP.png}
    \end{center}
    \caption{Gaussian Process}
\end{figure}
\pagebreak

\section{Report Session}
\paragraph{}This part is actually more like a flipped classroom. By reading the paper and teaching the paper to our students, I not only gained the academic ideas and skills in the paper, but more importantly, I looked up a lot of materials through the reading of the paper, and understood the brain-computer interface technology from a deeper perspective. And through the narration, I have a deeper understanding of the knowledge in the class.

\paragraph{}For example, my report topic is a painting system using a hybrid control approch based on SSVEP and P300. By reading this paper, I not only have a better understanding of the application of SSVEP and P300 potential in today's brain-computer interface technology, but also have learned a lot of mathematical methods for EEG signal processing algorithms from this paper, such as CCA, LDA and so on. At the same time, I also learned a lot from the structure of this paper, such as how to design the experiment, the selection of the experimenter, the final processing of the experimental data and the comparison with other models, etc.

\paragraph{}Similarly, I also learned a lot from listening to the reports of my classmates later. For example, the cochlear implant connected with the brain nerve can increase the number of sound channels, so that more clear sounds can be transmitted to the "ears" of hearing-impaired people. For example, some students reported on the method of using an intrusive interface to make people who have had a stroke no longer suffer. Some other students chose a similar topic to me, and also used some existing EEG signals to paint and type. These reports are very fantastic.

\paragraph{}At the same time, some suggestions from teacher Shi in class are also very important. For example, a student did not understand the meaning of the indicator ITR in class, and misinterpreted Information Translate Rate as Information Transmit Rate. At that time, I just didn't understand the meaning of this index here. Later, after the teacher's guidance, I realized that it was because our classmates made a mistake. At the same time, Teacher Shi's comments also made us feel enlightened. For example, he mentioned some basic signal processing algorithms, and he would talk about them, which helped us understand the working principle of the improved algorithm.

\paragraph{}Moreover, Teacher Shi will also point out some problems in our report and urge us to correct them. For example, as the first time I made an academic report on the stage, I forgot to write the source of the paper and the corresponding literature code of the cited literature on the front page of the slide. Teacher Shi also pointed out in time. And then the slides of my academic report will not forget the source of the paper.

\paragraph{}However, I think there are still some problems in this report link. For example, the interaction between the students who report and the students who listen to the lecture is not very strong, so we can consider making the students on and off the stage linkage. At the same time, I think our report students can also find some literature related to coding, and then add some code running links when presenting on the stage. I think this can be closer to the characteristics of our engineering courses, and also improve the coding ability of our students.

\paragraph{}But in general, I learned a lot of new knowledge from this session, and I realized that there are so many researchers in the world working on the development of brain-computer interface technology. It also gives me confidence that humans can decipher the secrets of the human brain.
\pagebreak
\section{Summary}
\paragraph{}I learned a lot in this BCI class. From the introduction,
the teacher's slides and explanation have established great interest for me,
which makes me very motivated to continue studying this course. From the initial
EEG collection to the use of modern deep learning technique, and machine learning methods
for signal processing. This not only introduced me to the technological frontiers of modern
brain-computer interfaces but also made me realize that the intelligent algorithms and techniques
of artificial intelligence that we learn in our regular classes can be applied to so many fields.
\paragraph{}In the course, I followed the teacher's teaching path, not only learning the knowledge
in the textbook but also expanding from the textbook, I also learned other knowledge from the
Internet. For example, the progress in BCI technology in universities in China and the progress in the understanding of
BCI in laboratories around the world all these encouraging developments make me believe that soon, domestic BCI
technology will be further developed with the support of China's "Brain Project".
\paragraph{}In addition, I also benefited a lot from the program that the teacher conducted
for students to share their papers. Various new technologies shared by the students made
me see another side of brain-computer interface technology. For example, the new cochlear
implant enables the hearing impaired to hear external sounds at a higher frequency through
the deep learning technology in the cochlea. And the use of EEG recognition to allow
paralyzed people to control computers to do what they want.
\paragraph{}In a word, this brain-computer interface not only broadened my horizon but
also allowed me to gain a lot of knowledge.But I do have a small suggestion. At present,
all our courses are taught by means of PowerPoint, textbook and etc. which are actually quite monotonous in sensory
experience. So I wondered if our students could go and experience the existing
brain-computer interface devices. Generate some of your own EEG data, and then write the
corresponding program to analyze it. I think this class will be very interesting.
\paragraph{}Thank you, teachers and tas, for all your work this semester.


\pagebreak
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